Convolutional neural networks (CNNs) are known to outperform traditional fully-connected ANNs, and this has been demonstrated in various domains, including physics-based simulations [1]}, [2]}, [3]}, [4]}. CNNs work on the principle of parameter sharing and local convolution operations, which enables efficient training on large inputs. Their disadvantage is that the inputs/outputs of CNNs are restricted to grid inputs, such as images, videos, or structured FE meshes. However, CNNs have found their successors, the graph neural networks (GNNs), that can work with any structure of inputs/outputs.
